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from ..base_handler import ModelHandler
from transformers import AutoTokenizer
import torch
import time
import numpy as np
from scipy.stats import spearmanr
from sklearn.metrics.pairwise import cosine_similarity
class EmbeddingModelHandler(ModelHandler):
def __init__(self, model_name, model_class, quantization_type, test_text):
super().__init__(model_name, model_class, quantization_type, test_text)
self.tokenizer = AutoTokenizer.from_pretrained(model_name)
def run_inference(self, model, text):
inputs = self.tokenizer(text, return_tensors='pt', truncation=True, padding=True).to(self.device)
start_time = time.time()
with torch.no_grad():
outputs = model(**inputs)
end_time = time.time()
return outputs, end_time - start_time
def decode_output(self, outputs):
return outputs.last_hidden_state.mean(dim=1).cpu().numpy()
def compare_outputs(self, original_outputs, quantized_outputs):
"""Compare outputs for embedding models"""
if original_outputs is None or quantized_outputs is None:
return None
original_embeds = original_outputs.last_hidden_state.cpu().numpy()
quantized_embeds = quantized_outputs.last_hidden_state.cpu().numpy()
metrics = {
'mse': ((original_embeds - quantized_embeds) ** 2).mean(),
'cosine_similarity': cosine_similarity(
original_embeds.reshape(1, -1),
quantized_embeds.reshape(1, -1)
)[0][0],
'correlation': spearmanr(
original_embeds.flatten(),
quantized_embeds.flatten()
)[0],
'norm_difference': np.abs(
np.linalg.norm(original_embeds) -
np.linalg.norm(quantized_embeds)
)
}
return metrics |